Comparison
deeplake vs infinity
Verdict
Pick deeplake if deeplake is an AI Data Runtime for Agents designed with serverless Postgres and multimodal data lake support, targeting scalable retrieval and training capabilities; pick infinity if designed for high-speed hybrid searches in LLM applications, infinity supports dense vector, sparse vector, tensor, and full-text data types.
Markdown twin · deeplake alternatives · infinity alternatives
GraphCanon updated today
vs
Trust & integrity
| Signal | deeplake | infinity |
|---|---|---|
| Maintenance | Steady (87d since push) As of 4d · github_public_v1 | Very active (3d since push) As of today · github_public_v1 |
| Provenance | Not a fork · Organization account As of 4d · github_public_v1 | Not a fork · Organization account As of today · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of 1mo · osv@v1 | No lockfile (source not queried) As of 1mo · osv@v1 |
| deps.dev advisories | Not queried deps.dev@v1 | Not queried deps.dev@v1 |
| OpenSSF Scorecard | Not queried openssf-scorecard@v1 | Not queried openssf-scorecard@v1 |
Tagline
- deeplake
- AI Data Runtime for Agents with scalable retrieval and training features
- infinity
- AI-native database for LLM applications offering fast hybrid search capabilities.
Stars
- deeplake
- 9.2k
- infinity
- 4.7k
Forks
- deeplake
- 721
- infinity
- 437
Open issues
- deeplake
- 63
- infinity
- 64
Language
- deeplake
- C++
- infinity
- C++
Adopt for
- deeplake
- Deeplake is an AI Data Runtime for Agents designed with serverless Postgres and multimodal data lake support, targeting scalable retrieval and training capabilities.
- infinity
- Designed for high-speed hybrid searches in LLM applications, infinity supports dense vector, sparse vector, tensor, and full-text data types.
Persona
- deeplake
- -
- infinity
- -
Runtime
- deeplake
- -
- infinity
- -
License
- deeplake
- Deeplake uses the Apache-2.0 license, allowing free use in both open source and commercial projects with attribution.
- infinity
- Apache-2.0
Last pushed
- deeplake
- May 21, 2026
- infinity
- Aug 17, 2026
Categories
- deeplake
- Data & Retrieval, Model Training, Vector Databases
- infinity
- Data & Retrieval, Vector Databases
Trust and health
Maintenance
- deeplake
- Steady (60%)
- infinity
- Very active (96%)
Days since push
- deeplake
- 87d
- infinity
- 3d
Open issues (now)
- deeplake
- 63
- infinity
- 64
Stars delta
- deeplake
- +16 (30d)
- infinity
- +51 (30d)
Open issues delta
- deeplake
- -6 (30d)
- infinity
- -2 (30d)
Full report
- deeplake
- Trust report
- infinity
- Trust report
Shared compatibility
- Python · deeplake: Python runtime · infinity: Python runtime
Choose deeplake if…
- Pricing: Pricing details are not specified for Deeplake's public repository..
- Requirements: Deeplake can be installed using pip, making it accessible via the command `pip install deeplake`..
- Tags unique to deeplake: agent, agentic-rag, ai, computer-vision.
- Also covers Model Training.
- When you are developing applications that require seamless integration with AI agents, as Deeplake supports agent-centric design.
When NOT to use deeplake
- If your project does not benefit from an agent-centric architecture and you primarily require traditional database operations without multimodal features.
- When cost control is critical and serverless PostgreSQL might introduce variable costs compared to on-premises solutions for data retrieval and training.
Choose infinity if…
- Tags unique to infinity: ai-native, approximate-nearest-neighbor-search, bm25, cpp20.
- When your application requires rapid hybrid search capabilities across multiple data types including tensors and full texts.
- More recently updated (last pushed Aug 17, 2026).
When NOT to use infinity
- If your project does not benefit from fast hybrid search features or if you prefer not to use an AI-native database solution.
- When support for only dense vectors is sufficient, and the added complexity of supporting tensors and full texts is unnecessary.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (activeloopai/deeplake) · observed Aug 17, 2026
- GitHub forks (activeloopai/deeplake) · observed Aug 17, 2026
- Last push (activeloopai/deeplake) · observed May 21, 2026
- License file (Apache-2.0) · observed Aug 17, 2026
- Decision facts (enrichment) · observed Jul 9, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (infiniflow/infinity) · observed Aug 21, 2026
- GitHub forks (infiniflow/infinity) · observed Aug 21, 2026
- Last push (infiniflow/infinity) · observed Aug 17, 2026
- License file (Apache-2.0) · observed Aug 21, 2026
- Decision facts (enrichment) · observed Jul 14, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: deeplake 9.2k · infinity 4.7k (synced Aug 17, 2026).
Common questions
- What is the difference between deeplake and infinity?
- deeplake: AI Data Runtime for Agents with scalable retrieval and training features. infinity: AI-native database for LLM applications offering fast hybrid search capabilities.. See the comparison table for live GitHub stats and shared categories.
- When should I choose deeplake over infinity?
- Choose deeplake over infinity when Pricing: Pricing details are not specified for Deeplake's public repository.; Requirements: Deeplake can be installed using pip, making it accessible via the command
pip install deeplake.; Tags unique to deeplake: agent, agentic-rag, ai, computer-vision; Also covers Model Training; When you are developing applications that require seamless integration with AI agents, as Deeplake supports agent-centric design. - When should I choose infinity over deeplake?
- Choose infinity over deeplake when Tags unique to infinity: ai-native, approximate-nearest-neighbor-search, bm25, cpp20; When your application requires rapid hybrid search capabilities across multiple data types including tensors and full texts; More recently updated (last pushed Aug 17, 2026).
- When should I avoid deeplake?
- If your project does not benefit from an agent-centric architecture and you primarily require traditional database operations without multimodal features. When cost control is critical and serverless PostgreSQL might introduce variable costs compared to on-premises solutions for data retrieval and training.
- When should I avoid infinity?
- If your project does not benefit from fast hybrid search features or if you prefer not to use an AI-native database solution. When support for only dense vectors is sufficient, and the added complexity of supporting tensors and full texts is unnecessary.
- Is deeplake or infinity more popular on GitHub?
- deeplake has more GitHub stars (9,224 vs 4,675). Stars measure visibility, not whether either tool fits your constraints.
- Are deeplake and infinity open source?
- Yes - both are open-source projects on GitHub (deeplake: Apache-2.0, infinity: Apache-2.0).
- Where can I find alternatives to deeplake or infinity?
- GraphCanon lists graph-backed alternatives at deeplake alternatives and infinity alternatives (deeplake markdown twin, infinity markdown twin), ranked by typed relationship edges rather than popularity votes.
- Is there a machine-readable version of this comparison?
- Yes. The markdown twin at this comparison mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.
- Which is better maintained, deeplake or infinity?
- deeplake: Steady. infinity: Very active. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance.
- Where are the full trust reports for deeplake and infinity?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: deeplake trust report; infinity trust report.